We next evaluate the performance of our models on a challenging fine-grained image classification task of recognizing various animal special in the iNaturalist18  [1]} dataset. We further evaluate how well the models generalize on a wildlife monitoring (and preservation as a result) task iWildCam-WILDS  [2]}. This dataset has real-world geographic shift as the images are taken by camera traps all over the world and across different camera traps, there is drastic variation in illumination, color, camera angle, background, vegetation, and relative animal frequencies, which makes this dataset challenging We note that iWildCam-WILDS dataset enables us to test the practical application of computer vision research in wildlife preservation effort where the models are used to recognize animal species (if any) in the camera trap..
